Longjiao Zhang — accepted AI-conference papers

2025

Efficient Dynamic Graphs Learning with Refined Batch Parallel Training

Zhengzhao Feng, Rui Wang, Longjiao Zhang, Tongya Zheng, Ziqi Huang, Mingli Song

IJCAI 2025

Memory-based temporal graph neural networks (MTGNN) use node memory to store historical information, enabling efficient processing of large dynamic graphs through batch parallel training, with larger batch sizes leading to increased training efficiency. However, this approach overlooks the interdepe

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2025

SALoM: Structure Aware Temporal Graph Networks with Long-Short Memory Updater

Hanwen Liu, Longjiao Zhang, Rui Wang, Tongya Zheng, Sai Wu, Chang Yao +1

NeurIPS 2025poster

Dynamic graph learning is crucial for accurately modeling complex systems by integrating topological structure and temporal information within graphs. While memory-based methods are commonly used and excel at capturing short-range temporal correlations, they struggle with modeling long-range depende…

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